Browsing by Author "Alam, Edris"

Now showing 1 - 20 of 29

A systematic review of the nexus between climate change and social media: present status, trends, and future challenges
(Scopus, 2024-10-14) Sultana, Bebe Chand; Prodhan, Md. Tabiur Rahman; Alam, Edris; Sohel, Md. Salman; Bari, A. B. M. Mainul; Pal, Subodh Chandra; Islam, Abu Reza Md. Towfiqul; Islam, Md. Kamrul
: Social media and climate change are some of the most controversial issues of the 21st century. Despite numerous studies, our understanding of current social media trends, popular hot topics, and future challenges related to climate change remains significantly limited. This research presents a systematic review of climate change and social media for the first time. Review the studies published between 2009 and 2022 in places like Google Scholar, Science Direct, Web-of-Science, Scopus, ResearchGate, and others. For this systematic review, we found 1,057 articles. Forty-five articles were the most relevant according to our goals and study design, which followed the PRISMA framework. The results of this review demonstrate that Twitter is the most popular platform. Every year, we identify rising trends in the number of publications. Past studies often focused on just one social media site, like Twitter (n = 26) or Facebook (n = 5). Although most studies focus on the United States, the study area is primarily “all over the world.” This study offers a theoretical framework by examining the relationship between social media platforms and the discourse surrounding climate change. It looked into how social media trends influence public perception, raise awareness, and spur action on climate change. In practical terms, the study focuses on important and trending topics like nonbelievers and climate change. The contribution consists of synthesizing the body of research, providing insights into the state of the digital world, and suggesting future lines of inquiry for the field of social media and climate change studies. We highlighted the studies’ quality assessment result of “moderate quality.” This systematic review provides information about how climate change is now portrayed on social media and lays the groundwork for further study in this area.
A systematic review of the nexus between climate change and social media: present status, trends, and future challenges
(Scopus, 2024-10-14) Sultana, Bebe Chand; Prodhan, Md. Tabiur Rahman; Alam, Edris; Sohel, Md. Salman; Bari, A. B. M. Mainul; Pal, Subodh Chandra; Islam, Md. Kamrul; Islam, Abu Reza Md. Towfiqul
Social media and climate change are some of the most controversial issues of the 21st century. Despite numerous studies, our understanding of current social media trends, popular hot topics, and future challenges related to climate change remains significantly limited. This research presents a systematic review of climate change and social media for the first time. Review the studies published between 2009 and 2022 in places like Google Scholar, Science Direct, Web-of-Science, Scopus, ResearchGate, and others. For this systematic review, we found 1,057 articles. Forty-five articles were the most relevant according to our goals and study design, which followed the PRISMA framework. The results of this review demonstrate that Twitter is the most popular platform. Every year, we identify rising trends in the number of publications. Past studies often focused on just one social media site, like Twitter (n = 26) or Facebook (n = 5). Although most studies focus on the United States, the study area is primarily “all over the world.” This study offers a theoretical framework by examining the relationship between social media platforms and the discourse surrounding climate change. It looked into how social media trends influence public perception, raise awareness, and spur action on climate change. In practical terms, the study focuses on important and trending topics like nonbelievers and climate change. The contribution consists of synthesizing the body of research, providing insights into the state of the digital world, and suggesting future lines of inquiry for the field of social media and climate change studies. We highlighted the studies’ quality assessment result of “moderate quality.” This systematic review provides information about how climate change is now portrayed on social media and lays the groundwork for further study in this area.
Anthropogenic Drivers Induced Desertification Under Changing Climate
(Elsevier, 2023-12-17) Pal, Subodh Chandra; Chatterjee, Uday; Chakrabortty, Rabin; Roy, Paramita; Chowdhuri, Indrajit; Saha, Asish; Islam, Abu Reza Md. Towfiqul; Alam, Edris; Islam, Md Kamrul
A study of the extended desertification due to anthropogenic causes under climate change (CC) associated with its impact is presented here. Desertification, the main environmental issue, severely impacts agricultural output, causing poverty and economic instability in a nation like India. The regional distribution of desertification was determined using the RF and MaxEnt models. The western, central, and southern portions of the nation are very high, high, and moderately susceptible to desertification, respectively, according to the RF model. The MaxEnt model indicates that the western, central, and southern parts of the country exhibit a significant susceptibility to desertification, with the eastern parts also showing a moderate level of vulnerability. The remaining portion of this region, mainly in the north, east, and northeast, is particularly resistant to desertification. The outcome demonstrated that the country's desertification process had expanded from the west to the south. However, there are some spatial differences associated with the mentioned part of the country. This relevant information is crucial for decision maker of this country to take suitable remedies in regard to the reduction of the intensity of desertification.
Application of Bagging and Boosting Ensemble Machine Learning Techniques for Groundwater Potential Mapping in a Drought-prone Agriculture Region of Eastern India
(Springer, 2024-09-02) Halder, Krishnagopal; Srivastava, Amit Kumar; Ghosh, Anitabha; Nabik, Ranajit; Pan, Subrata; Chatterjee, Uday; Bisai, Dipak; Pal, Subodh Chandra; Zeng, Wenzhi; Ewert, Frank; Gaiser, Thomas; Pande, Chaitanya Baliram; Islam, Abu Reza Md. Towfiqul; Alam, Edris; Islam, Md Kamrul
Groundwater is a primary source of drinking water for billions worldwide. It plays a crucial role in irrigation, domestic, and industrial uses, and significantly contributes to drought resilience in various regions. However, excessive groundwater discharge has left many areas vulnerable to potable water shortages. Therefore, assessing groundwater potential zones (GWPZ) is essential for implementing sustainable management practices to ensure the availability of groundwater for present and future generations. This study aims to delineate areas with high groundwater potential in the Bankura district of West Bengal using four machine learning methods: Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Voting Ensemble (VE). The models used 161 data points, comprising 70% of the training dataset, to identify significant correlations between the presence and absence of groundwater in the region. Among the methods, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) proved to be the most effective in mapping groundwater potential, suggesting their applicability in other regions with similar hydrogeological conditions. The performance metrics for RF are very good with a precision of 0.919, recall of 0.971, F1-score of 0.944, and accuracy of 0.943. This indicates a strong capability to accurately predict groundwater zones with minimal false positives and negatives. Adaptive Boosting (AdaBoost) demonstrated comparable performance across all metrics (precision: 0.919, recall: 0.971, F1-score: 0.944, accuracy: 0.943), highlighting its effectiveness in predicting groundwater potential areas accurately; whereas, Extreme Gradient Boosting (XGBoost) outperformed the other models slightly, with higher values in all metrics: precision (0.944), recall (0.971), F1-score (0.958), and accuracy (0.957), suggesting a more refined model performance. The Voting Ensemble (VE) approach also showed enhanced performance, mirroring XGBoost's metrics (precision: 0.944, recall: 0.971, F1-score: 0.958, accuracy: 0.957). This indicates that combining the strengths of individual models leads to better predictions. The groundwater potentiality zoning across the Bankura district varied significantly, with areas of very low potentiality accounting for 41.81% and very high potentiality at 24.35%. The uncertainty in predictions ranged from 0.0 to 0.75 across the study area, reflecting the variability in groundwater availability and the need for targeted management strategies. In summary, this study highlights the critical need for assessing and managing groundwater resources effectively using advanced machine learning techniques. The findings provide a foundation for better groundwater management practices, ensuring sustainable use and conservation in Bankura district and beyond.
Artificial Neural Network-Based Land Use-Specific Carbon Patterns and Their Effects on Land Surface Temperature as a Result of the Rohingya Refugee Influx
(IEEE, 2023-12-21) Sarkar, Showmitra Kumar; Saroar, MD. Mustafa; Das, Palash Chandra; Chakraborty, Tanmoy; Rudra, Rhyme Rubayet; Alam, Edris; Islam, MD. Kamrul; Islam, Abu Reza Md. Towfiqul
The objective of the research is to investigate how refugees’ influx has altered the carbon dynamics of different land uses and the relationship between land use specific carbon emissions and land surface temperature (LST). Two upazilais of the Cox’s Bazar district, Bangladesh (i.e., Ukhiya and Teknaf), were mostly affected by the Rohingya refugee influx and are the focus of the study. The study classified the land use land cover (LULC) into four classes (e.g., agricultural, forest, settlement, and water) for two different time periods (i.e., before and after the influx of Rohingya refugees) using an artificial neural network algorithm and sentinel satellite imagery. Carbon emissions and absorptions specific to land use were calculated using classified land use land cover and coefficients. Again, two time series of Landsat 8 imagery were applied to estimate land surface temperature shifts. The area of forests was found to have decreased by 21.19 square miles (9.58 percent) and the area of settlements to have increased by 18.24 square miles (8.25 percent) between 2017 and 2021. There was a negative net land-use based carbon emission of -5187.02 tons per year in 2017. In 2021, it was predicted that annual net emissions would total 2208.24 tons. LST during the study period has increased as a result of human activities that release greenhouse gases into the atmosphere. The findings of this research will inform policymakers’ decisions about the conservation and sustainable development of natural resources in the region experiencing an influx of Rohingya refugees.
Assessing Seismicity in Bangladesh: An Application of Guttenberg-Richter Relationship and Spectral Analysis
(Taylor & Francis Group, 2023-08-21) Islam, Abu Reza Md. Towfiqul; Akter, Mst. Yeasmin; Amanat, Sumaia; Alam, Edris; Sultana, Mst. Laila; Shahid, Shamsuddin; Das, Arnob; Peu, Susmita Datta; Mallick, Javed
Bangladesh has a high risk of earthquakes because the Dauki, Jamuna, and Chittagong-Myanmar faults are still active. However, the assessment of seismicity remains a big challenge due to the complex geologic setting of Bangladesh. This study employed the Guttenberg-Richter relationship and the spectral models to assess and analyze the earthquake conditions in Bangladesh. Besides, an instrumental earthquake catalogue, obtained from the Bangladesh Meteorological Department (BMD), covering 1985–2017, is established. The results revealed that the Guttenberg-Richter constants of a and b were 2.981 and 0.392, which propagated a strain release from 1992 to 2017. The spectral model analyses, e.g. wavelet transform (WT), short-time Fourier transformation (STFT), and multitaper model (MTM), demonstrated the magnitude and strain release anomalies of the same magnitude ranging from 4.8 to 5.7, indicating the probable precursor of an upcoming earthquake. Notably, magnitudes have been running around 4.5–5.8, which may act as a signal to major earthquakes that have not been evident before. The proposed models allowed for the completion of the Bangladesh earthquake catalogue and provided a platform for future seismicity assessment and earthquake probability analysis. These results should be considered in determining how likely earthquakes are to happen in an area or region.
Assessment of Soil Heavy Metal Pollution and Associated Ecological Risk of Agriculture Dominated Mid-Channel Bars in a Subtropical River Basin
(Springer Nature Limited, 2023-07-09) Hoque, Md. Mofizul; Islam, Aznarul; Islam, Abu Reza Md. Towfiqul; Pal, Subodh Chandra; Mahammad, Sadik; Alam, Edris
The elevated concentrations of heavy metals in soil considerably threaten ecological and human health. To this end, the present study assesses metals pollution and its threat to ecology from the mid-channel bar’s (char) agricultural soil in the Damodar River basin, India. For this, the contamination factor (CF), enrichment factor (EF), geoaccumulation index (Igeo), pollution index, and ecological risk index (RI) were measured on 60 soil samples at 30 stations (2 from each station, i.e., surface and sub-surface) in different parts of the mid-channel bar. The CF and EF indicate that both levels of char soil have low contamination and hence portray a higher potential for future enrichment by heavy metals. Moreover, Igeo portrays that soil samples are uncontaminated to moderately contaminated. Further, pollution indices indicate that all the samples (both levels) are unpolluted with a mean of 0.062 for surface soils and 0.048 for sub-surface soils. Both levels of the char have a low potentiality for ecological risk with an average RI of 0.20 for the surface soils and 0.19 for the sub-surface soils. Moreover, Technique for order preference by similarity to ideal solution (TOPSIS) indicates that the sub-surface soils have lower pollution than the surface soils. The geostatistical modeling reveals that the simple kriging technique was estimated as the most appropriate interpolation model. The present investigation exhibits that reduced heavy metal pollution is due to the sandy nature of soils and frequent flooding. However, the limited pollution is revealed due to the intensive agricultural practices on riverine chars. Therefore, this would be helpful to regional planners, agricultural engineers, and stakeholders in a basin area.
Bioaccumulation and sources of metal(loid)s in fish species from a subtropical river in Bangladesh: a public health concern
(Scopus, 2024-12-07) Ali, Mir Mohammad; Kubra, Khadijatul; Alam, Edris; Mondol, Anwar Hossain; Islam, Md. Saiful; Karim, Ehsanul; Ahmed, A. S. Shafiuddin; Siddique, Md. Abu Bakar; Malafaia, Guilherme; Ahmed, A. S. Shafiuddin; Siddique, Md. Abu Bakar
Toxic metals and freshwater fish’s metalloid contamination are significant environmental concerns for overall public health. However, the bioaccumulation and sources of metal(loids) in freshwater fishes from Bangladesh still remain unknown. Thus, the As, Pb, Cd, and Cr concentrations in various freshwater fish species from the Rupsha River basin were measured, including Tenualosa ilisha, Gudusia chapra, Otolithoides pama, Setipinna phasa, Mystus vittatus, Glossogobius giuris, and Pseudeutropius atherinoides. An atomic absorption spectrophotometer was used to determine metal concentrations. The mean concentrations of metal(loids) in the fish muscle (mg/kg) were found to be As (1.53) > Pb (1.25) > Cr (0.51) > Cd (0.39) in summer and As (1.72) > Pb (1.51) > Cr (0.65) > Cd (0.49) in winter. The analyzed fish species had considerably different metal(loid) concentrations with seasonal variation, and the distribution of the metals (loids) was consistent with the normal distribution. The demersal species, M. vittatus, displayed the highest bio-accumulative value over the summer. However, in both seasons, none of the species were bio-accumulative. According to multivariate statistical findings, the research area’s potential sources of metal(loid) were anthropogenic activities linked to geogenic processes. Estimated daily intake, target hazard quotient (THQ), and carcinogenic risk (CR) were used to assess the influence of the risk on human health. The consumers’ THQs values were < 1, indicating that there were no non-carcinogenic concerns for local consumers. Both categories of customers had CRs that fell below the permissible range of 1E − 6 to 1E − 4, meaning they were not at any increased risk of developing cancer. The children’s group was more vulnerable to both carcinogenic and non-carcinogenic hazards. Therefore, the entry of metal(loids) must be regulated, and appropriate laws must be used by policymakers
Change in Cyclone Disaster Vulnerability and Response in Coastal Bangladesh
(John Wiley & Sons, 2023-08-13) Alam, Edris; Collins, Andrew E.; Islam, Abu Reza Md. Towfiqul; Paul, Alak; Islam, Md Kamrul
The number of deaths owing to tropical cyclones in Bangladesh has significantly reduced. Category 4 Cyclone Gorky in 1991 and Sidr in 2007 caused 147,000 and 4,500 deaths respectively, whereas Category 1 Cyclone Mora in 2017 resulted in six. Face-to-face interviews with 362 residents, participant observation, and focus-group discussions answer a research question about how change in coastal areas has contributed to this outcome. The study considered institutional approaches of disaster risk management through legal frameworks, administrative arrangements, cyclone preparedness activities, cyclone detection and early warning dissemination, construction of shelter centres, strengthening of various types of coastal embankments, paved roads, and pre-cyclone evacuation. The findings indicate significant improvement in house structures and design, income levels and diversification, education, awareness, individual capacity, poverty reduction, and lowering dependency on agriculture-based earning. Furthermore, the availability of mobile telephones, radio, television, and social media platforms enhanced social connectivity and greater gender equality and empowerment helped to facilitate disaster preparedness, evacuation, and response.
Change in cyclone disaster vulnerability and response in coastal Bangladesh
(Scopus, 2023-08-13) Alam, Edris; Collins, Andrew E.; Islam, Abu Reza Md. Towfiqul; Paul, Alak; Islam, Md Kamrul
The number of deaths owing to tropical cyclones in Bangladesh has significantly reduced. Category 4 Cyclone Gorky in 1991 and Sidr in 2007 caused 147,000 and 4,500 deaths respectively, whereas Category 1 Cyclone Mora in 2017 resulted in six. Face-to-face interviews with 362 residents, participant observation, and focus-group discussions answer a research question about how change in coastal areas has contributed to this outcome. The study considered institutional approaches of disaster risk management through legal frameworks, administrative arrangements, cyclone preparedness activities, cyclone detection and early warning dissemination, construction of shelter centres, strengthening of various types of coastal embankments, paved roads, and pre-cyclone evacuation. The findings indicate significant improvement in house structures and design, income levels and diversification, education, awareness, individual capacity, poverty reduction, and lowering dependency on agriculture-based earning. Furthermore, the availability of mobile telephones, radio, television, and social media platforms enhanced social connectivity and greater gender equality and empowerment helped to facilitate disaster preparedness, evacuation, and response.
Change in Cyclone Disaster Vulnerability and Response in Coastal Bangladesh
(John Wiley & Sons, 2023-01-15) Alam, Edris; Collins, Andrew E.; Islam, Abu Reza Md. Towfiqul; Paul, Alak; Islam, Md Kamrul
The number of deaths owing to tropical cyclones in Bangladesh has significantly reduced. Category 4 Cyclone Gorky in 1991 and Sidr in 2007 caused 147,000 and 4,500 deaths respectively, whereas Category 1 Cyclone Mora in 2017 resulted in six. Face-to-face interviews with 362 residents, participant observation, and focus-group discussions answer a research question about how change in coastal areas has contributed to this outcome. The study considered institutional approaches of disaster risk management through legal frameworks, administrative arrangements, cyclone preparedness activities, cyclone detection and early warning dissemination, construction of shelter centres, strengthening of various types of coastal embankments, paved roads, and pre-cyclone evacuation. The findings indicate significant improvement in house structures and design, income levels and diversification, education, awareness, individual capacity, poverty reduction, and lowering dependency on agriculture-based earning. Furthermore, the availability of mobile telephones, radio, television, and social media platforms enhanced social connectivity and greater gender equality and empowerment helped to facilitate disaster preparedness, evacuation, and response.
Climate Change in Bangladesh: Temperature and Rainfall Climatology of Bangladesh for 1949–2013 and Its Implication on Rice Yield
(PLOS ONE, 2023-12-06) Alam, Edris; Hridoy, Al-Ekram Elahee; Tusher, Shekh Md. Shajid Hasan; Islam, Abu Reza Md. Towfiqul; Islam, Md Kamrul
Bangladesh has been ranked as one of the world’s top countries affected by climate change, particularly in terms of agricultural crop sector. The purpose of this study is to identify spatial and temporal changes and trends in long-term climate at local and national scales, as well as their implications for rice yield. In this study, Modified Mann-Kendall and Sen’s slope tests were used to detect significant trends and the magnitude of changes in temperature and rainfall. The temperature and rainfall data observed and recorded at 35 meteorological stations in Bangladesh over 65-years in the time span between the years 1949 and 2013 have been used to detect these changes and trends of variation. The results show that mean annual Tmean, Tmin, and Tmax have increased significantly by 0.13°C, 0.13°C, and 0.13°C/decade, respectively. The most significant increasing trend in seasonal temperatures for the respective Tmean, Tmin, and Tmax was 0.18°C per decade (post-monsoon), 0.18°C/decade (winter), and 0.23°C/decade (post-monsoon), respectively. Furthermore, the mean annual and pre-monsoon rainfall showed a significant increasing trend at a rate of 4.20 mm and 1.35 mm/year, respectively. This paper also evaluates climate variability impacts on three major rice crops, Aus, Aman, and Boro during 1970–2013. The results suggest that crop yield variability can be explained by climate variability during Aus, Aman, and Boro seasons by 33, 25, and 16%, respectively. Maximum temperature significantly affected the Aus and Aman crop yield, whereas rainfall significantly affected all rice crops’ yield. This study sheds light on sustainable agriculture in the context of climate change, which all relevant authorities should investigate in order to examine climate-resilient, high-yield crop cultivation.
Coupling of Machine Learning and Remote Sensing for Soil Salinity Mapping in Coastal Area of Bangladesh
(Springer, 2023-10-10) Sarkar, Showmitra Kumar; Rudra, Rhyme Rubayet; Sohan, Abid Reza; Das, Palash Chandra; Ekram, Khondaker Mohammed Mohiuddin; Talukdar, Swapan; Rahman, Atiqur; Alam, Edris; Islam, Md Kamrul; Islam, Abu Reza Md. Towfiqul
Soil salinity is a pressing issue for sustainable food security in coastal regions. However, the coupling of machine learning and remote sensing was seldom employed for soil salinity mapping in the coastal areas of Bangladesh. The research aims to estimate the soil salinity level in a southwestern coastal region of Bangladesh. Using the Landsat OLI images, 13 soil salinity indicators were calculated, and 241 samples of soil salinity data were collected from a secondary source. This study applied three distinct machine learning models (namely, random forest, bagging with random forest, and artificial neural network) to estimate soil salinity. The best model was subsequently used to categorize soil salinity zones into five distinct groups. According to the findings, the artificial neural network model has the highest area under the curve (0.921), indicating that it has the most potential to predict and detect soil salinity zones. The high soil salinity zone covers an area of 977.94 km2 or roughly 413.51% of the total study area. According to additional data, a moderate soil salinity zone (686.92 km2) covers 30.56% of Satkhira, while a low soil salinity zone (582.73 km2) covers 25.93% of the area. Since increased soil salinity adversely affects human health, agricultural production, etc., the study's findings will be an effective tool for policymakers in integrated coastal zone management in the southwestern coastal area of Bangladesh.
Delineating the drought vulnerability zones in Bangladesh
(Scopus, 2024-10-26) Sarkar, Showmitra Kumar; Das, Swadhin; Rudra, Rhyme Rubayet; Ekram, Khondaker Mohammed Mohiuddin; Haydar, Mafrid; Alam, Edris; Islam, Md Kamrul
The research aims to explore the vulnerability of Bangladesh to drought by considering a comprehensive set of twenty-four factors, classified into four major categories: meteorological, hydrological, agricultural, and socioeconomic vulnerability. To achieve this, the study utilized a knowledge-based multi-criteria method known as the Analytic Hierarchy Process (AHP) to delineate drought vulnerability zones across the country. Weight estimation was accomplished by creating pairwise comparison matrices for factors and different types of droughts, drawing on relevant literature, field experience, and expert opinions. Additionally, online-based interviews and group discussions were conducted with 30 national and foreign professionals, researchers, and academics specializing in drought-related issues in Bangladesh. Results from overall drought vulnerability map shows that the eastern hills region displays a notably high vulnerability rate of 56.85% and an extreme low vulnerability rate of 0.03%. The north central region shows substantial vulnerability at high levels (35.85%), while the north east exhibits a significant proportion (41.68%) classified as low vulnerability. The north west region stands out with a vulnerability rate of 40.39%, emphasizing its importance for drought management strategies. The River and Estuary region displays a modest vulnerability percentage (38.44%), suggesting a balanced susceptibility distribution. The south central and south east regions show significant vulnerabilities (18.99% and 39.60%, respectively), while the south west region exhibits notable vulnerability of 41.06%. The resulting model achieved an acceptable level of performance, as indicated by an area under the curve value of 0.819. Policymakers and administrators equipped with a comprehensive vulnerability map can utilize it to develop and implement effective drought mitigation strategies, thereby minimizing the losses associated with drought.
Future Changes of Summer Monsoon Rainfall and Temperature Over Bangladesh Using 27 CMIP6 Models
(Informa UK Limited, 2023-11-27) Bhattacharjee, Arnob; Hassan, S. M. Quamrul; Hazra, Papri; Kormoker, Tapos; Islam, Shahana; Alam, Edris; Islam, Md Kamrul; Islam, Abu Reza Md. Towfiqul
This research aims to investigate the future changes in summer monsoon rainfall and temperature in Bangladesh. The study revealed that INM-CM5-0 is the best model for projecting temperature, while BCC-CSM2-MR is the best model for projecting rainfall over Bangladesh. Using data from a large ensemble of 27 models from CMIP6, the study examined the rainfall and temperature change projections of Bangladesh during the twenty first century relative to the reference period (1981–2014) under SSP2–4.5 and SSP5–8.5. Under SSP2-4.5 and SSP5-8.5, the multi-model ensemble monsoon mean rainfall over Bangladesh will fluctuate between 40 and 260 mm and 100 and 900 mm, respectively. In most parts of the country’s north, northeastern, and western regions, the projected changes in spatial patterns of monsoon rainfall indicate an increase in rainfall. The projected temperature indicated that Bangladesh’s northwest and west-central areas could face the most significant rise in temperatures, surpassing 3.8 °C under SSP5-8.5.
Future Groundwater Potential Mapping Using Machine Learning Algorithms and Climate Change Scenarios in Bangladesh
(Springer Nature, 2024-05-06) Sarkar, Showmitra Kumar; Rudra, Rhyme Rubayet; Talukdar, Swapan; Das, Palash Chandra; Nur, Md. Sadmin; Alam, Edris; Islam, Md Kamrul; Islam, Abu Reza Md. Towfiqul
The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study’s results to inform evidence-based choices on water management and planning at the national level.
Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh
(2024-05-06) Kumar Sarkar, Showmitra; Rudra, Rhyme Rubayet; Talukdar, Swapan; Chandra Das, Palash; Sadmin Nur, Md.; Alam, Edris; Kamrul Islam, Md; Md. Towfiqul Islam, Abu Reza
The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study’s results to inform evidence-based choices on water management and planning at the national level.
Hydro-chemical based assessment of groundwater vulnerability in the Holocene multi-aquifers of Ganges delta
(2024-01-13) Saha, Asish; Chandra Pal, Subodh; Md. Towfiqul Islam, Abu Reza; Islam, Aznarul; Alam, Edris; Kamrul Islam, Md.
Determining the degree of high groundwater arsenic (As) and fluoride (F−) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F− concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F− contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.
Hydro-chemical Based Assessment of Groundwater Vulnerability in the Holocene Multi-aquifers of Ganges Delta
(Springer Nature, 2024-01-02) Saha, Asish; Pal, Subodh Chandra; Islam, Abu Reza Md. Towfiqul; Islam, Aznarul; Alam, Edris; Islam, Md. Kamrul
Determining the degree of high groundwater arsenic (As) and fluoride (F−) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F− concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F− contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.
Land Use and Climate Change-Induced Soil Erosion Mapping in a Sub-Tropical Environment
(Informa UK Limited, trading as Taylor & Francis Group., 2023-10-27) Pal, Subodh Chandra; Chakrabortty, Rabin; Islam, Abu Reza Md. Towfiqul; Roy, Paramita; Chowdhuri, Indrajit; Saha, Asish; Islam, Aznarul; Costache, Romulus; Alam, Edris
One of the most important aspects of the ‘sub-tropical’ monsoon-influenced environment is the issue of ‘soil erosion’ and its related ‘land degradation’. On the other hand, the climate in this area has become quite extreme. According to this viewpoint, it is important to research a future ‘soil erosion’ scenario in front of the probable effects of climate change and land use change. For the objective of assessing the extent of soil erosion in this area, this study took into account both the USLE and the RUSLE. Compared to the USLE that has been validated, RUSLE has a comparatively greater quantitative efficiency. In RUSLE, the ‘very high’ (>20) and ‘high’ (15–20) ‘soil erosion’ zones tend to be associated with the ‘north-western, western, south-western, and southern’ regions of the river basin. The ‘soil erosion’ that will occur in the future has been estimated by taking into account the projected rainfall, land use and land cover (LULC). ‘Soil erosion’ has increased from the previous time to the projected time. Predicted R factor values for SSP 585 range from 399.92 to 493.72. In addition, a growing erosion tendency associated with increased shared socio-economic pathways (SSPs) has been found.